Fast Bayesian People Detection

Template-based methods have been shown to be effective at solving the problem of tracking specific objects, but their large number of free parameters can make them slow to apply and hard to optimise globally. In this work, we propose a template-based method for tracking people with fixed cameras, which automatically detects the number of people in a frame, is robust to occlusions, and can run at near-real-time frame rates. We demonstrate the effectiveness of the method by comparing it to a state-of-the-art background segmentation algorithm and show its important performance advantage

Assessing Acceptance of Assistive Social Agent Technology by Older Adults: the Almere Model

This paper proposes a model of technology acceptance that is specifically developed to test the acceptance of assistive social agents by elderly users. The research in this paper develops and tests an adaptation and theoretical extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) by explaining intent to use not only in terms of variables related to functional evaluation like perceived usefulness and perceived ease of use, but also variables that relate to social interaction. The new model was tested using controlled experiment and longitudinal data collected regarding three different social agents at elderly care facilities and at the homes of older adults. The model was strongly supported accounting for 59-79% of the variance in usage intentions and 49-59% of the variance in actual use. These findings contribute to our understanding of how elderly users accept assistive social agents

Fast Bayesian People Detection

Abstract Template-based methods have been shown to be effective at solving the problem of tracking specific objects, but their large number of free parameters can make them slow to apply and hard to optimise globally. In this work, we propose a template-based method for tracking people with fixed cameras, which automatically detects the number of people in a frame, is robust to occlusions, and can run at near-realtime frame rates. We demonstrate the effectiveness of the method by comparing it to a state-of-the-art background segmentation algorithm and show its important performance advantage

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data

Assistive technology design and development for acceptable robotics companions for ageing years

© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe

Effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older individuals after hip fracture, the SO-HIP trial::study protocol of a three-arm stepped wedge cluster randomized trial

BACKGROUND: The performance of activities of daily living (ADL) at home is important for the recovery of older individuals after hip fracture. However, 20-90% of these individuals lose ADL function and never fully recover. It is currently unknown to what extent occupational therapy (OT) with coaching based on cognitive behavioral treatment (CBT) improves recovery. The same holds for sensor monitoring-based coaching in addition to OT. Here, we describe the design of a study investigating the effect of sensor monitoring embedded in an OT rehabilitation program on the recovery of ADL among older individuals after hip fracture. METHODS/ DESIGN: Six nursing homes will be randomized in a three-arm stepped wedge cluster randomized trial. All nursing homes will initially provide standard care. At designated time points, nursing homes, successively and in random order, will cross over to the provision of OT and at the next time point, to sensor monitoring-enhanced OT. A total of 288 older individuals, previously living alone in the community, who after a hip fracture were admitted to a geriatric rehabilitation ward for a short-term rehabilitation, will be enrolled. Individuals in the first intervention group (OTc) will participate in an OT rehabilitation program with coaching based on cognitive behavioral therapy (CBT) principles. In the sensor monitoring group, sensor monitoring is added to the OT intervention (OTcsm). Participants will receive a sensor monitoring system consisting of (i) an activity monitor during nursing home stay, (ii) a sensor monitoring system at home and a (iii) a web-based feedback application. These components will be embedded in the OT. The OT consists of a weekly session with an occupational therapist during the nursing home stay followed by four home visits and four telephone consultations. The primary outcome is patient-perceived daily functioning at 6 months, assessed using the Canadian Occupational Performance Measure (COPM). DISCUSSION: As far as we know, this study is the first large-scale stepped wedge trial, studying the effect of sensor monitoring embedded in an OT coaching program. The study will provide new knowledge on the combined intervention of sensor monitoring and coaching in OT as a part of a rehabilitation program to enable older individuals to perform everyday activities and to remain living independently after hip fracture. TRIAL REGISTRATION NUMBER: Netherlands National Trial Register, NTR 5716 Date registered: April 1 2016

Physical connections and cooperation in swarm robotics

We describe a new multi-robot system, named SWARM-BOTS, that exploits physical inter-connections to solve tasks that are impossible for a single robot. This is for instance the case of passing large gaps or high steps in all-terrain conditions. In order to achieve this type of autonomous collective operations, the design of the type of connection, as well as its sensors and actuators, plays a key role. This paper presents the choices made in the SWARM-BOTS project and the know-how collected until now. The requirements for autonomous operation and mobility of each robots have led to the development of a connectivity very different those found in selfrecon gurable robots. Some of the solutions employed for this problem are inspired upon physical connectivity of social insects. We also illustrate with two experiments how sensors and actuators allow autonomous operation in connection, release as well as passive and active exploitation of inter-robot degrees of freedom (DOF)